{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Multilayer Perceptron (MLP) \n",
    "(c) Deniz Yuret, 2019\n",
    "* Objectives: Stack multiple layers, demonstrate the power of nonlinear activation functions, use regularization and dropout to address overfitting.\n",
    "* Prerequisites: [Linear models](30.lin.ipynb), [MNIST](20.mnist.ipynb)\n",
    "* New functions: \n",
    "[relu](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.relu), \n",
    "[dropout](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.dropout), \n",
    "[param, param0](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.param), \n",
    "[xavier](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.xavier), \n",
    "[training](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.training), \n",
    "[gc](http://denizyuret.github.io/Knet.jl/latest/reference/#Knet.gc)\n",
    "\n",
    "![image](https://github.com/denizyuret/Knet.jl/blob/master/docs/src/images/neural_net2.jpeg?raw=true)\n",
    "([image source](http://cs231n.github.io/neural-networks-1))\n",
    "\n",
    "A multilayer perceptron, i.e. a fully connected feed-forward neural network, is basically a bunch of linear layers stuck together with non-linearities in between. In this notebook we will first chain together a couple of linear layers and see that it has no effect on performance (multiple linear layers still compute a linear function). However with a one line change (adding an elementwise nonlinear function between layers) the accuracy will go from 92% to 98%! It turns out the class of functions that can be represented by linear models is severely restricted and does not contain good MNIST classifiers. MLPs have no such restrictions, being **universal function approximators** they can represent any classifier given large enough layers and learn the correct classifier with sufficient data. \n",
    "\n",
    "Simple models with low capacity, e.g. the linear model, have an **underfitting** problem: the error does not drop to 0 even on the training set. Models with high capacity, e.g. MLP, have an **overfitting** problem: even when the training set error goes to 0, the test set error remains high. We will implement two methods to fight overfitting: **regularization** and **dropout**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling Knet [1902f260-5fb4-5aff-8c31-6271790ab950]\n",
      "└ @ Base loading.jl:1278\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KnetArray{Float32,N} where N"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set display width, load packages, import symbols\n",
    "ENV[\"COLUMNS\"]=72\n",
    "using Knet: Knet, dir, accuracy, progress, sgd, load, save, gc, Param, KnetArray, Data, minibatch, nll, relu, training, dropout # param, param0, xavier_uniform\n",
    "using Statistics: mean\n",
    "using Base.Iterators: flatten\n",
    "using IterTools: ncycle, takenth\n",
    "using MLDatasets: MNIST\n",
    "import CUDA # functional\n",
    "array_type=(CUDA.functional() ? KnetArray{Float32} : Array{Float32})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load MNIST data\n",
    "xtrn,ytrn = MNIST.traindata(Float32); ytrn[ytrn.==0] .= 10\n",
    "xtst,ytst = MNIST.testdata(Float32);  ytst[ytst.==0] .= 10\n",
    "dtrn = minibatch(xtrn, ytrn, 100; xsize = (784,:), xtype = array_type)\n",
    "dtst = minibatch(xtst, ytst, 100; xsize = (784,:), xtype = array_type);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trainresults (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For running experiments\n",
    "function trainresults(file,model; o...)\n",
    "    if (print(\"Train from scratch? \"); readline()[1]=='y')\n",
    "        r = ((model(dtrn), model(dtst), 1-accuracy(model,dtrn), 1-accuracy(model,dtst))\n",
    "             for x in takenth(progress(sgd(model,ncycle(dtrn,100))),length(dtrn)))\n",
    "        r = reshape(collect(Float32,flatten(r)),(4,:))\n",
    "        Knet.save(file,\"results\",r)\n",
    "        GC.gc(true) # To save gpu memory\n",
    "    else\n",
    "        isfile(file) || download(\"http://people.csail.mit.edu/deniz/models/tutorial/$file\",file)\n",
    "        r = Knet.load(file,\"results\")\n",
    "    end\n",
    "    println(minimum(r,dims=2))\n",
    "    return r\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "xavier_uniform (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some utilities to make model definitions easier:\n",
    "param(d...; init=xavier_uniform, atype=array_type)=Param(atype(init(d...)))\n",
    "param0(d...; atype=array_type)=param(d...; init=zeros, atype=atype)\n",
    "xavier_uniform(o,i) = (s = sqrt(2/(i+o)); 2s .* rand(o,i) .- s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A generic multilayer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Let's define a chain of layers\n",
    "struct Chain\n",
    "    layers\n",
    "    Chain(layers...) = new(layers)\n",
    "end\n",
    "(c::Chain)(x) = (for l in c.layers; x = l(x); end; x)\n",
    "(c::Chain)(x,y) = nll(c(x),y)\n",
    "(c::Chain)(d::Data) = mean(c(x,y) for (x,y) in d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Multiple linear layers do not improve over a single linear layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Define a linear layer (See lin.ipynb):\n",
    "struct Layer0; w; b; end\n",
    "Layer0(i::Int,o::Int) = Layer0(param(o,i),param0(o))\n",
    "(l::Layer0)(x) = (l.w * x .+ l.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(\"64×784 Param{KnetArray{Float32,2}}\", \"64-element Param{KnetArray{Float32,1}}\")\n",
      "(\"10×64 Param{KnetArray{Float32,2}}\", \"10-element Param{KnetArray{Float32,1}}\")\n"
     ]
    }
   ],
   "source": [
    "# Here is an example two layer model\n",
    "model=Chain(Layer0(784,64), Layer0(64,10))\n",
    "println.(summary.((l.w,l.b)) for l in model.layers);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> y\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┣████████████████████┫ [100.00%, 60000/60000, 01:46/01:46, 565.90i/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Float32[0.24216889; 0.28485858; 0.06973334; 0.0797]\n"
     ]
    }
   ],
   "source": [
    "# Train the two layer model\n",
    "# 2.04e-01  100.00%┣████████████████┫ 60000/60000 [00:51/00:51, 1179.52i/s]\n",
    "# [0.240742; 0.282535; 0.0691333; 0.0794]\n",
    "mlp1 = trainresults(\"mlp113a.jld2\", model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(\"10×784 Param{KnetArray{Float32,2}}\", \"10-element Param{KnetArray{Float32,1}}\")\n"
     ]
    }
   ],
   "source": [
    "# Here is a single layer (linear) model\n",
    "model=Chain(Layer0(784,10))\n",
    "println.(summary.((l.w,l.b)) for l in model.layers);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> n\n",
      "Float32[0.24184321; 0.26703557; 0.066866666; 0.0747]\n"
     ]
    }
   ],
   "source": [
    "# Train the single layer (linear) model\n",
    "# 2.30e-01  100.00%┣████████████████┫ 60000/60000 [00:40/00:40, 1502.26i/s]\n",
    "# [0.242363; 0.267073; 0.0671333; 0.0746]\n",
    "lin1 = trainresults(\"mlp113b.jld2\", model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "using Plots; default(fmt=:png,ls=:auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multilinear converges to a similar solution, not identical because problem is non-convex\n",
    "plot([lin1[1,:], lin1[2,:], mlp1[1,:], mlp1[2,:]], ylim=(0.0,0.4),\n",
    "    labels=[\"trnLin\" \"tstLin\" \"trnMulti\" \"tstMulti\"],xlabel=\"Epochs\",ylabel=\"Loss\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# error results also close to the linear model\n",
    "plot([lin1[3,:], lin1[4,:], mlp1[3,:], mlp1[4,:]], ylim=(0.0,0.12),\n",
    "    labels=[\"trnLin\" \"tstLin\" \"trnMulti\" \"tstMulti\"], xlabel=\"Epochs\", ylabel=\"Error\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Multiple linear layers are useless because they are equivalent to a single linear layer\n",
    "If we write down what is being computed and do some algebra, we can show that what is being computed is still an affine function of the input, i.e. stacking multiple linear layers does not increase the representational capacity of the model:\n",
    "\n",
    "\\begin{align*}\n",
    "\\hat{p} &= \\mbox{soft}(W_2 (W_1 x + b_1) + b_2) \\\\\n",
    "&= \\mbox{soft}((W_2 W_1)\\, x + W_2 b_1 + b_2) \\\\\n",
    "&= \\mbox{soft}(W x + b)\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Multi Layer Perceptron (MLP) adds non-linearities between layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Using nonlinearities between layers results in a model with higher capacity and helps underfitting\n",
    "# relu(x)=max(0,x) is a popular function used for this purpose, it replaces all negative values with zeros.\n",
    "struct Layer1; w; b; f; end\n",
    "Layer1(i::Int,o::Int,f=relu) = Layer1(param(o,i),param0(o),f)\n",
    "(l::Layer1)(x) = l.f.(l.w * x .+ l.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> n\n",
      "Float32[0.0051907585; 0.07693735; 0.00013333333; 0.0218]\n"
     ]
    }
   ],
   "source": [
    "# We add a nonlinear activation function to all but the last layer\n",
    "# 2.83e-02  100.00%┣████████████████┫ 60000/60000 [00:52/00:52, 1150.34i/s]\n",
    "# [0.00636037; 0.084129; 0.000516667; 0.0243]\n",
    "model = Chain(Layer1(784,64), Layer1(64,10,identity))\n",
    "mlp2 = trainresults(\"mlp113c.jld2\", model);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## MLP solves underfitting but still has an overfitting problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MLP solves the underfitting problem!  A more serious overfitting problem remains.\n",
    "plot([lin1[1,:], lin1[2,:], mlp2[1,:], mlp2[2,:]], ylim=(0.0,0.4),\n",
    "     labels=[\"trnLin\" \"tstLin\" \"trnMLP\" \"tstMLP\"], xlabel=\"Epochs\", ylabel=\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test error improves from 7.5% to 2.5%!\n",
    "plot([lin1[3,:], lin1[4,:], mlp2[3,:], mlp2[4,:]], ylim=(0.0,0.12),\n",
    "    labels=[\"trnLin\" \"tstLin\" \"trnMLP\" \"tstMLP\"], xlabel=\"Epochs\",ylabel=\"Error\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## MLP with L1/L2 regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# We add two new fields for L1 and L2 regularization\n",
    "struct Chain2\n",
    "    layers; λ1; λ2\n",
    "    Chain2(layers...; λ1=0, λ2=0) = new(layers, λ1, λ2)\n",
    "end\n",
    "\n",
    "# The prediction and average loss do not change\n",
    "(c::Chain2)(x) = (for l in c.layers; x = l(x); end; x)\n",
    "(c::Chain2)(d::Data) = mean(c(x,y) for (x,y) in d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# The loss function penalizes the L1 and/or L2 norms of parameters during training\n",
    "function (c::Chain2)(x,y)\n",
    "    loss = nll(c(x),y)\n",
    "    if training() # Only apply regularization during training, only to weights, not biases.\n",
    "        c.λ1 != 0 && (loss += c.λ1 * sum(sum(abs, l.w) for l in c.layers))\n",
    "        c.λ2 != 0 && (loss += c.λ2 * sum(sum(abs2,l.w) for l in c.layers))\n",
    "    end\n",
    "    return loss\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> n\n",
      "Float32[0.027542168; 0.08254478; 0.0071; 0.0243]\n"
     ]
    }
   ],
   "source": [
    "# 1.94e-01  100.00%┣████████████████┫ 60000/60000 [00:57/00:57, 1058.05i/s]\n",
    "# [0.026522; 0.0802438; 0.00688333; 0.0231]\n",
    "model = Chain2(Layer1(784,64), Layer1(64,10,identity); λ1=4f-5)\n",
    "mlp3 = trainresults(\"mlp113d.jld2\", model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overfitting less, test loss improves from 0.0841 to 0.0802\n",
    "plot([mlp2[1,:], mlp2[2,:], mlp3[1,:], mlp3[2,:]], ylim=(0.0,0.15),\n",
    "     labels=[\"trnMLP\" \"tstMLP\" \"trnL1\" \"tstL1\"],xlabel=\"Epochs\",ylabel=\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test error also improves: 0.0243 -> 0.0231\n",
    "plot([mlp2[3,:], mlp2[4,:], mlp3[3,:], mlp3[4,:]], ylim=(0.0,0.04),\n",
    "     labels=[\"trnMLP\" \"tstMLP\" \"trnL1\" \"tstL1\"],xlabel=\"Epochs\",ylabel=\"Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## MLP with dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{verbatim}\n",
       "dropout(x, p; drop, seed)\n",
       "\\end{verbatim}\n",
       "Given an array \\texttt{x} and probability \\texttt{0<=p<=1} return an array \\texttt{y} in which each element is 0 with probability \\texttt{p} or \\texttt{x[i]/(1-p)} with probability \\texttt{1-p}. Just return \\texttt{x} if \\texttt{p==0}, or \\texttt{drop=false}. By default \\texttt{drop=true} in a \\texttt{@diff} context, \\texttt{drop=false} otherwise.  Specify a non-zero \\texttt{seed::Number} to set the random number seed for reproducible results. See \\href{http://www.jmlr.org/papers/v15/srivastava14a.html}{(Srivastava et al. 2014)} for a reference.\n",
       "\n"
      ],
      "text/markdown": [
       "```\n",
       "dropout(x, p; drop, seed)\n",
       "```\n",
       "\n",
       "Given an array `x` and probability `0<=p<=1` return an array `y` in which each element is 0 with probability `p` or `x[i]/(1-p)` with probability `1-p`. Just return `x` if `p==0`, or `drop=false`. By default `drop=true` in a `@diff` context, `drop=false` otherwise.  Specify a non-zero `seed::Number` to set the random number seed for reproducible results. See [(Srivastava et al. 2014)](http://www.jmlr.org/papers/v15/srivastava14a.html) for a reference.\n"
      ],
      "text/plain": [
       "\u001b[36m  dropout(x, p; drop, seed)\u001b[39m\n",
       "\n",
       "  Given an array \u001b[36mx\u001b[39m and probability \u001b[36m0<=p<=1\u001b[39m return an array \u001b[36my\u001b[39m in which\n",
       "  each element is 0 with probability \u001b[36mp\u001b[39m or \u001b[36mx[i]/(1-p)\u001b[39m with probability\n",
       "  \u001b[36m1-p\u001b[39m. Just return \u001b[36mx\u001b[39m if \u001b[36mp==0\u001b[39m, or \u001b[36mdrop=false\u001b[39m. By default \u001b[36mdrop=true\u001b[39m in a\n",
       "  \u001b[36m@diff\u001b[39m context, \u001b[36mdrop=false\u001b[39m otherwise. Specify a non-zero \u001b[36mseed::Number\u001b[39m\n",
       "  to set the random number seed for reproducible results. See\n",
       "  (Srivastava et al. 2014)\n",
       "  (http://www.jmlr.org/papers/v15/srivastava14a.html) for a reference."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@doc dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Dropout is another way to address overfitting\n",
    "struct Layer2; w; b; f; pdrop; end\n",
    "Layer2(i::Int,o::Int,f=relu; pdrop=0) = Layer2(param(o,i),param0(o),f,pdrop)\n",
    "(l::Layer2)(x) = l.f.(l.w * dropout(x,l.pdrop) .+ l.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> n\n",
      "Float32[0.042467445; 0.0909296; 0.013483333; 0.0249]\n"
     ]
    }
   ],
   "source": [
    "# 1.06e-01  100.00%┣████████████████┫ 60000/60000 [00:53/00:53, 1142.58i/s]\n",
    "# [0.0144843; 0.067272; 0.00376667; 0.0189]\n",
    "model = Chain(Layer2(784,64,pdrop=0.2), Layer2(64,10,identity))\n",
    "mlp4 = trainresults(\"mlp113e.jld2\", model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overfitting less, loss results improve 0.0841 -> 0.0673\n",
    "plot([mlp2[1,:], mlp2[2,:], mlp4[1,:], mlp4[2,:]], ylim=(0.0,0.15),\n",
    "     labels=[\"trnMLP\" \"tstMLP\" \"trnDrop\" \"tstDrop\"],xlabel=\"Epochs\",ylabel=\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this time error also improves 0.0243 -> 0.0189\n",
    "plot([mlp2[3,:], mlp2[4,:], mlp4[3,:], mlp4[4,:]], ylim=(0.0,0.04),\n",
    "     labels=[\"trnMLP\" \"tstMLP\" \"trnDrop\" \"tstDrop\"],xlabel=\"Epochs\",ylabel=\"Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(mlperr = 0.0218f0, L1err = 0.0243f0, dropouterr = 0.0249f0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(mlperr=minimum(mlp2[4,:]),L1err=minimum(mlp3[4,:]),dropouterr=minimum(mlp4[4,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(mlploss = 0.07693735f0, L1loss = 0.08254478f0, dropoutloss = 0.0909296f0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(mlploss=minimum(mlp2[2,:]),L1loss=minimum(mlp3[2,:]),dropoutloss=minimum(mlp4[2,:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## MLP with larger hidden layer and dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train from scratch? stdin> n\n",
      "Float32[0.027398968; 0.07152775; 0.008916667; 0.0196]\n"
     ]
    }
   ],
   "source": [
    "# The current trend is to use models with higher capacity tempered with dropout\n",
    "# 5.80e-02  100.00%┣████████████████┫ 60000/60000 [00:54/00:54, 1101.25i/s]\n",
    "# [0.0035497; 0.0479644; 0.0003; 0.0149]\n",
    "model = Chain(Layer2(784,256,pdrop=0.2), Layer2(256,10,identity))\n",
    "mlp = trainresults(\"mlp113f.jld2\", model);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Both train and test loss is better with the larger model\n",
    "plot([mlp4[1,:], mlp4[2,:], mlp[1,:], mlp[2,:]],ylim=(0,0.15),\n",
    "    labels=[\"trn64\" \"tst64\" \"trn256\" \"tst256\"],xlabel=\"Epochs\",ylabel=\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are down to 1.5% error.\n",
    "plot([mlp4[3,:], mlp4[4,:], mlp[3,:], mlp[4,:]],ylim=(0,0.04),\n",
    "    labels=[\"trn64\" \"tst64\" \"trn256\" \"tst256\"],xlabel=\"Epochs\",ylabel=\"Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "julia.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Julia 1.5.0",
   "language": "julia",
   "name": "julia-1.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
